Dans cette communication, nous dérivons une nouvelle borne de Cramér-Rao intrinsèque pour paramètres et observations vivant sur groupes de Lie. L'expression est obtenue en utilisant les propriétés inhérentes à leur structure. De plus, une expression exacte est obtenue dans le cas où paramètres et observations sont sur SO(2), le groupe de Lie des matrices de rotations en 2D. La borne proposée est alors validée numériquement pour un modèle gaussien définie sur le groupe de Lie SO(2).

La détection d’anomalies dans le domaine de la surveillance maritime est un enjeu majeur pour la sécurité des navires et des nations. De nombreux algorithmes de détection d’anomalies sont disponibles dans la littérature mais ils ne sont pas tous adaptés à l’analyse de séries temporelles comme les trajectoires de navires, en particulier lorsque ces trajectoires n’ont pas la même longueur. Cet article présente une manière d’adapter l’algorithme One-Class SVM à la détection d’anomalies dans des séries temporelles associées à des trajectoires de navires à l’aide d’un noyau basé sur une mesure de similarité de type “dynamic time warping”.

Les solutions basées sur l’apprentissage automatique pour la démodulation et le décodage de canal ont gagné en popularité ces dernières années en raison de leur capacité à traiter de gros volumes de données, de leur faible latence et de leur robustesse face aux erreurs de modèle. Dans ce contexte, des travaux antérieurs ont appliqué les Machines à Vecteurs de Support (SVM) au problème de décodage, produisant des résultats favorables pour les codes très courts. Dans ce travail, nous introduisons une approche nouvelle, bit-par-bit, qui réduit considérablement la complexité du décodeur basé sur les SVM, ainsi que la taille de l’ensemble de données d’entraînement. Enfin, nous analysons la complexité de notre système et montrons que les décodeurs basés sur les SVM restent des solutions hautement complexes qui ne sont pas encore applicables aux longueurs de codes plus grandes.

Différents spectromètres ou imageurs hyperspectraux sont utilisés afin de déterminer la concentration de certains gaz au niveau de l’atmosphère. La détermination de la réponse spectrale de ces instruments est un problème important car une connaissance imparfaite peut induire des erreurs dans les concentrations de gaz obtenues. Dans cet article, nous proposons une nouvelle méthode d’estimation de la réponse spectrale d’un spectromètre basée sur un algorithme de type Orthogonal Matching Pursuit (OMP) qui permet de décomposer cette réponse dans un dictionnaire de fonctions de base de manière parcimonieuse.

We define a novel core network router scheduling architecture called priority switching scheduler (PSS), to carry and isolate time constrained and elastic traffic flows from best-effort traffic. To date, one possible solution has been to implement a core DiffServ network with standard fair queuing and scheduling mechanisms as proposed in the well-known “A Differentiated Services Code Point (DSCP) for Capacity-Admitted Traffic” from RFC5865. This architecture is one of the most selected solutions by internet service provider for access networks (e.g., customer-premises equipment) and deployed within several performance-enhancing proxies (PEPs) over satellite communications (SATCOM) architectures. In this study, we argue that the proposed standard implementation does not allow to efficiently quantify the reserved capacity for the AF class. By using a novel credit-based shaper mechanism called burst limiting shaper (BLS) to manage the AF class, we show that PSS can provide the same isolation for the time constrained EF class while better quantifying the part allocated to the AF class. PSS operates both when the output link capacity is fixed (e.g., wire links and terrestrial networks) or might vary due to system impairments or weather condition (e.g., wireless or satellite links). We demonstrate the capability of PSS through an emulated SATCOM scenario with variable capacity and show the AF output rate is less dependent on the EF traffic, which improves the quantification of the reserved capacity of AF, without impacting EF traffic.

Global navigation satellite systems (GNSS) are a key player in a plethora of applications, ranging from navigation and timing, to Earth observation or space weather characterization. For navigation purposes, interference scenarios are among the most challenging operation conditions, which clearly impact the maximum likelihood estimates (MLE) of the signal synchronization parameters. While several interference mitigation techniques exist, a theoretical analysis on the GNSS MLE performance degradation under interference, being fundamental for system/receiver design, is a missing tool. The main goal of this contribution is to provide such analysis, by deriving closed-form expressions of the misspecified Cramér-Rao (MCRB) bound and estimation bias, for a generic GNSS signal corrupted by an interference. The proposed bias and MCRB expressions are validated for a linear frequency modulation chirp signal interference.

Low-frequency radio interferometry is crucial to understanding the universe and its very early days. Unfortunately, most of the current instruments are ground-based and thus impacted by the interferences massively produced by the Earth. To alleviate this issue, scientific missions aim at using Moonorbiting nano-satellite swarms as distributed radio-telescopes in outer space, keeping them out of Earth interference range. However, swarms of nano-satellites are systems with complex dynamics and need to be appropriately characterized to achieve their scientific mission. This paper presents a methodology based on graph theory for characterizing the swarm network system by computing graph theory metrics around three properties: the node density, network connectivity and ISL availability. We show that these properties are well-suited for highlighting a possible heterogeneity in a network and adapt a routing strategy accordingly. This work is the first milestone in defining the best-suited routing strategy within the swarm from the derived network properties.

We address the problem of partitioning a network of nano-satellites to distribute fairly the network load under energy consumption constraints. The study takes place in a context where this swarm of nano-satellites orbits the Moon and works as, but not limited to, a distributed radio-telescope for low-frequency radio interferometry. During an interferometry mission, each nano-satellite collects observation data, then shares them with the other swarm members to compute a global image of space. However, the simultaneous transmission of large volumes of data can cause communication issues by overloading the radio channel, leading to potential packet loss. In this context, we investigate three division algorithms based on graph sampling techniques. We prove that random walk-based algorithms overall perform the best in terms of conservation of graph properties and fairness for group sizes down to 10% of the original graph.

In the last decade, it has been quickly recognized that backhauling Low Power Wide Area Networks (LPWAN) through Low Earth Orbit (LEO) satellites paves the way to the development of novel applications for a truly ubiquitous Internet of Things (IoT). Among LPWAN communications technologies, Narrowband IoT (NB-IoT) does not suffer from interference by other concurrent technologies since it works on a licensed frequency spectrum. At the same time, thanks to its medium access scheme based on contention resolution and resource allocation, NB-IoT is a key enabler for the specific market slice of IoT applications requiring a good level of reliability. In the architectural configuration analyzed throughout this contribution, an NB-IoT low power User Equipment (UE) can communicate with a LEO satellite equipped with an Evolved Node B (eNB) for a time limited to the visibility window of that satellite from the UE position on the Earth. However, the Doppler effect inherent to the time-varying relative speed of the eNB needs to be dealt with additional resources. The solutions proposed until now are non-trivial, thus making the use of NB- IoT for ground-to-satellite communications still expensive and energetically inefficient. Timely, this contribution proposes a procedure for a UE to infer the future values of the Doppler shift from the beacon signals so that frequency pre-compensation can be easily applied in the following interactions during the visibility time. The presented simulation results show that a UE needs to listen to about 10 beacon signals in 1 second to accurately and robustly predict the Doppler curve, thus enabling a lightweight (and eventually truly energy-efficient) implementation of NB-IoT over ground-to-satellite links.